A Transformation/Weighting Model for Estimating Michaelis-Menten Parameters,

Abstract

There has been considerable disagreement about how best to estimate the parameters in Michaelis-Menten models. This document points out that many fitting methods are based on different stochastic models, being weighted least squares estimates after appropriate transformation. The authors propose a flexible model which can be used to help determine the proper transformation and choice of weights. The method is illustrated by examples. Keywords: Nonlinear regression; Lineweaver Burke transformation.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1987
Accession Number
ADA186476

Entities

People

  • David Ruppert
  • Noel Cressie
  • Raymond J. Carroll

Organizations

  • University of North Carolina at Chapel Hill

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Classification
  • Computer Science
  • Data Science
  • Data Sets
  • Experimental Design
  • Fish
  • Identification
  • Information Science
  • Knowledge Management
  • Network Science
  • New York
  • Nonlinear Dynamics
  • Observation
  • Programming Languages
  • Regression Analysis
  • Security
  • Statistics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Approximation Theory.
  • Molecular and Cellular Biochemistry